Lab 5

Assignment 1

1.1

library(tm)
library(wordcloud)
library(RColorBrewer)

data_1<-read.table("Five.txt",header=F, sep='\n') #Read file
data_1$doc_id=1:nrow(data_1)
colnames(data_1)[1]<-"text"

#Here we interpret each line in the document as separate document
mycorpus_1 <- Corpus(DataframeSource(data_1)) #Creating corpus (collection of text data_1)
mycorpus_1 <- tm_map(mycorpus_1, removePunctuation)
mycorpus_1 <- tm_map(mycorpus_1, function(x) removeWords(x, stopwords("english")))
tdm_1 <- TermDocumentMatrix(mycorpus_1) #Creating term-document matrix
m_1 <- as.matrix(tdm_1)

#here we merge all rows
v_1 <- sort(rowSums(m_1),decreasing=TRUE) #Sum up the frequencies of each word
d_1 <- data.frame(word = names(v_1),freq=v_1) #Create one column=names, second=frequences
pal_1 <- brewer.pal(6,"Dark2")
pal_1 <- pal_1[-(1:2)] #Create palette of colors
wordcloud(d_1$word,d_1$freq, scale=c(8,.3),min.freq=2,max.words=100, random.order=F, rot.per=.15, colors=pal_1, vfont=c("sans serif","plain"))

data_2<-read.table("OneTwo.txt",header=F, sep='\n') #Read file
data_2$doc_id=1:nrow(data_2)
colnames(data_2)[1]<-"text"

#Here we interpret each line in the document as separate document
mycorpus_2 <- Corpus(DataframeSource(data_2)) #Creating corpus (collection of text data_1)
mycorpus_2 <- tm_map(mycorpus_2, removePunctuation)
mycorpus_2 <- tm_map(mycorpus_2, function(x) removeWords(x, stopwords("english")))
tdm_2 <- TermDocumentMatrix(mycorpus_2) #Creating term-document matrix
m_2 <- as.matrix(tdm_2)

#here we merge all rows
v_2 <- sort(rowSums(m_2),decreasing=TRUE) #Sum up the frequencies of each word
d_2 <- data.frame(word = names(v_2),freq=v_2) #Create one column=names, second=frequences
pal_2 <- brewer.pal(6,"Dark2")
pal_2 <- pal_2[-(1:2)] #Create palette of colors
wordcloud(d_2$word,d_2$freq, scale=c(8,.3),min.freq=2,max.words=100, random.order=F, rot.per=.15, colors=pal_2, vfont=c("sans serif","plain"))

First wordcloud corresponds to feedbacks of people who are pleased with the product. We can see that certain words are bigger than others which means they appear more number of times than other words in the wordcloud.By looking at the words we can get an idea about what features or aspects of the watch which pleased the customers. Here we see that the following features of the watch were found to be good by the customers-Face,looks,price,band,dial. We can also guess the features of the watch by looking at the words like dual,digital,analog,alarm,appearence,quality,water resistant,stopwatch,durability etc. Second wordcloud corresponds to feedbacks of people who are not pleased with the product. Here we see that the following features of the watch were found to be bad by the customers-Analog,battery,replacement.

1.2

Happy customers.Connector words-am,is,are,was,were

Happy customers.Connector words-a,the

Happy customers.Connector words-at

Happy customers.Connector words-of

Unhappy customers.Connector words-am,is,are,was,were

Unhappy customers.Connector words-a,the

Unhappy customers.Connector words-at

Unhappy customers.Connector words-of

Properties of this watch that are often mentioned

Appearence- Black, Gold and White face

Quality

cost

Display features- Analog and Digital display

Luminousity

Satisified customers are talking about

Good appearence

Simple and sporty look

Durability

Cheap pricing

Comfortable to wear

Durable watch band

Availability of different types of bands like Velcro band and metal band

Water resistant

Easy to read face

Toughness of the watch

Unsatisified customers are talking about

Low luminosity

Defective alarm

Buttons get broken and defective easily

Chronometer does not work properly

Low alarm sound

Analog display is defective

Tough to get the defective product replaced

Bad at keeping accurate time

Doesnt work when immersed deep in water

Good and Bad properties

Good properties

Appearence due to face and size

Cheap

Water resistant

Sporty look

Tough

Durable

Bad properties

Luminousity

Alarm

Bad quality buttons

Accuracy of time of Analog display

Replacement

Characteristics of the watch

Display- Digital and analog display

Luminous display

Face- Black,Gold or White

Water resitant

Sporty look

Velcro nylon or metal band

Chronometer

Assignment 2

library(plotly)
library(crosstalk)
library(tidyr)
library(ggplot2)
library(GGally)

olive <- read.csv("olive.csv")
d <- SharedData$new(olive)

2.1

scatterOlive <- plot_ly(d, x = ~linoleic, y = ~eicosenoic) %>%
  add_markers(color = I("black")) %>%
  layout(xaxis=list(title="Linoleic"), yaxis=list(title="Eicosenoic"))
scatterOlive

The values at the bottom of the plot have Eicosenoic values between one and three. There are 249 data points in the bottom part of the plot.

2.2

barOlive <- plot_ly(d, x=~as.factor(Region)) %>% add_histogram() %>% 
  layout(barmode="overlay",xaxis=list(title="Region"))

bscols(widths=c(2, NA),filter_slider("stearic", "values of stearic", d, ~stearic)
       ,subplot(scatterOlive,barOlive,titleY = TRUE,  titleX = TRUE)%>%
         highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%hide_legend())

values of stearic

All the observations which have eicosenoic values less then three are from the region two and three. Region two have linoleic value above 1050 and region three have below 1050.By using the slider it is found that most of the data in region 2 and 3 is between stearic values 180 to 280.We are using three interaction operators i.e. filtering operator for filtering stearic values, selection operator to select low values of Eicosenoic and connection operator to link the above plots together.

2.3

scatterOlive_2 <- plot_ly(d, x = ~linoleic, y = ~arachidic) %>%
  add_markers(color = I("black")) %>%
  layout(xaxis=list(title="Linoleic"), yaxis=list(title="Arachidic"))

subplot(scatterOlive,scatterOlive_2,  titleY = TRUE,  titleX = TRUE)%>%
  highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%hide_legend()

The Arachidic values which are less then 40 and less then 1050 on Linoleic scale are also at the bottom of the Eicosenoic VS Linoleic plot which are the outliers and they are grouped. Also two points which are greater then 1400 in Linoleic scale are also outliers in both plots which are not grouped.

2.4

p<-ggparcoord(olive, columns = c(4:11))
d<-plotly_data(ggplotly(p))%>%group_by(.ID)
d1<-SharedData$new(d, ~.ID, group="olive")
p1<-plot_ly(d1, x=~variable, y=~value)%>%add_lines(line=list(width=0.3))%>%
  add_markers(marker=list(size=0.3),text=~.ID, hoverinfo="text")

olive2=olive
olive2$.ID=1:nrow(olive)
d2<-SharedData$new(olive2, ~.ID, group="olive")
p2<-plot_ly(d2, x=~factor(Region) )%>%add_histogram()%>%layout(title = "Region",barmode="overlay")

ButtonsX=list()
for (i in 4:11){
  ButtonsX[[i-3]]= list(method = "restyle",
                        args = list( "x", list(olive[[i]])),
                        label = colnames(olive)[i])
}
ButtonsY=list()
for (i in 4:11){
  ButtonsY[[i-3]]= list(method = "restyle",
                        args = list( "y", list(olive[[i]])),
                        label = colnames(olive)[i])
}
ButtonsZ=list()
for (i in 4:11){
  ButtonsZ[[i-3]]= list(method = "restyle",
                        args = list( "z", list(olive[[i]])),
                        label = colnames(olive)[i])
}


p3 <- plot_ly(d2, x=~palmitic, y=~stearic, z=~oleic, alpha = 0.8) %>%
  add_markers() %>%
  layout(xaxis=list(title=""), yaxis=list(title=""), zaxis=list(title=""),
         title = "Select variable:",
         updatemenus = list(
           list(y=1.00, buttons = ButtonsX),
           list(y=0.85, buttons = ButtonsY),
           list(y=0.70, buttons = ButtonsZ)
         )  )

bscols(p1%>%highlight(on="plotly_select", dynamic=T, persistent = T, opacityDim = I(1))%>%
         hide_legend(),
       p3%>%highlight(on="plotly_click", dynamic=T, persistent = T)%>%hide_legend(),
       p2%>%highlight(on="plotly_click", dynamic=T, persistent = T)%>%hide_legend())

After brushing the different regions in the bar plot with different colours it was evident that region 1 can be defined using eicosenoic values. Linoliec values can be used to differentiate between region 2 and 3. All the linoliec values of region 2 are higher than that of region 3. Oleic values servers the same purpose. So these 3 variables can be used to define a region and can be considered influential variables. After brusing the different regions in diferent coulours finding clusters in the parallel co-ordinate plot beacame easier. There are 2 clusters in region 2 that can be seen when variable linoliec is observed. By selecting the influential variables in the dropboxesit can be seen that infact each region corresponds to one cluster.

Fig 1

2.5

The interaction operators used in the above plot are:

Selection

Connection

Navigation

Filtering operators can be used by providing drop boxes for parallel co-ordinate plot like in the case of 3d scatter plot.